Prescriptive analytics platform and polarity analysis engine

Abstract

Embodiments of the systems and methods disclosed herein provide a prescriptive analytics platform and polarity analysis engine in which a user can identify a target objective and use the system to find out whether the user's objectives are being met, what predictive factors are positively or negatively affecting the targeted objectives, as well as what recommended changes the user can make to better meet the objectives. The systems and methods may include a polarity analysis engine configured to determine the polarity of terms in free-text input in view of the target objective and the predictive factors and use the polarity to generate the recommended changes.

What is claimed is: 
     
       1. A prescriptive analytics system comprising:
 one or more electronic databases storing:
 a set of response data comprising a structured data set and a free text data set; and 
 an objective corresponding to the set of response data; 
 
 one or more hardware processors configured to execute computer-executable instructions in order to:
 access, from the one or more electronic databases, the set of response data; 
 identify predictive factors within the structured data set that are correlated to the objective; 
 generate a correlated structured data set based at least in part on the structured data set and the identified predictive factors; 
 generate a standardized lemma data set by converting terms within the free text data set into a plurality of lemmas; 
 generate a filtered lemma data set by filtering the standardized lemma data set to remove terms that are not associated with nouns or adjectives; 
 generate a scored lemma data set based at least in part on determining scores for the filtered lemma data set in view of the objective; 
 analyze the plurality of lemmas in the scored lemma data set to determine:
 occurrences of lemmas associated with a positive objective value; and 
 occurrences of lemmas associated with a negative objective value; 
 
 generate a distributed lemma data set by balancing distribution of the scored lemma data set against an aggregate frequency of terms in the scored lemma data set; and 
 assign polarity values to terms in the distributed lemma data set, wherein the polarity values indicate whether each of the terms is a positive term or a negative term based at least in part on a determination of each of the terms being associated with a degree of impact on one or more outcomes; 
 generate a recommendation action based at least in part on the polarity values. 
 
 
     
     
       2. The system of  claim 1 , wherein the set of response data is based at least in part on an aggregated customer feedback dataset. 
     
     
       3. The system of  claim 1 , wherein the identified predictive factors also indicate one or more behavior patterns associated with the first objective. 
     
     
       4. The system of  claim 1 , wherein the one or more hardware processors is further configured to:
 aggregate the filtered lemma data set with the correlated structured data set. 
 
     
     
       5. The computing system of  claim 1 , wherein the recommendation includes one or more actions that can be taken to achieve an improvement to the first objective. 
     
     
       6. The computing system of  claim 1 , wherein the generation of a scored lemma data set includes incorporating a user-specified amount of requested change in the objective. 
     
     
       7. The computing system of  claim 6 , wherein the user-specified amount of requested changes in the objective corresponds to a change to increase the amount of a positive response. 
     
     
       8. The computing system of  claim 6 , wherein the user-specified amount of requested changes in the objective corresponds to a change to decrease the amount of a negative response. 
     
     
       9. The computing system of  claim 1 , wherein the hardware processor is configured to generate a data packet that includes a graphical representation of at least a subset of the distributed lemma data set that includes a graphical representation of whether a term in the at least a subset is tied to a negative sentiment or a positive sentiment, the data packet configured for display on a remote computing device. 
     
     
       10. The computing system of  claim 1 , wherein the hardware processor is configured to generate a data packet that includes a graphical representation of at least a subset of the distributed lemma data set that includes a graphical representation of the frequency and strength of terms in the subset, the data packet configured for display on a remote computing device. 
     
     
       11. The computing system of  claim 1 , wherein the hardware processor is configured to generate a data packet that includes a graphical representation of at least a subset of the distributed lemma data set that includes a graphical representation of whether a term in the subset is tied to a negative sentiment or a positive sentiment and the frequency and strength of terms in the subset, the data packet configured for display on a remote device. 
     
     
       12. A computer-implemented method comprising:
 as implemented by one or more computing devices configured with specific computer-executable instructions,
 accessing, from one or more electronic databases, a set of response data comprising a structured data set and a free text data set; 
 identifying predictive factors within the structured data set that are correlated to an objective corresponding to the set of response data; 
 generating a correlated structured data set based at least in part on the structured data set and the identified predictive factors; 
 generating a standardized lemma data set by converting terms within the free text data set into a plurality of lemmas; 
 generating a filtered lemma data set by filtering the standardized lemma data set to remove terms that are not associated with nouns or adjectives; 
 generating a scored lemma data set based at least in part on determining scores for the filtered lemma data set in view of the objective; 
 analyzing the plurality of lemmas in the scored lemma data set to determine:
 occurrences of lemmas associated with a positive objective value; and 
 occurrences of lemmas associated with a negative objective value; 
 
 generating a distributed lemma data set by balancing distribution of the scored lemma data set against an aggregate frequency of terms in the scored lemma data set; and 
 assigning polarity values to terms in the distributed lemma data set, wherein the polarity values indicate whether each of the terms is a positive term or a negative term based at least in part on a determination of each of the terms being associated with a degree of impact on one or more outcomes; 
 generating a recommendation action based at least in part on the polarity values. 
 
 
     
     
       13. The computer-implemented method of  claim 12 , wherein the set of response data is based at least in part on an aggregated customer feedback dataset. 
     
     
       14. The computer-implemented method of  claim 12 , wherein the identified predictive factors also indicate one or more behavior patterns associated with the first objective. 
     
     
       15. The computer-implemented method of  claim 12 , further comprising:
 aggregating the filtered lemma data set with the correlated structured data set. 
 
     
     
       16. The computer-implemented method of  claim 12 , wherein the recommendation includes one or more actions that can be taken to achieve an improvement to the first objective. 
     
     
       17. Non-transitory computer storage having stored thereon a computer program, the computer program including executable instructions that instruct a computer system to at least:
 access, from one or more electronic databases, a set of response data comprising a structured data set and a free text data set; 
 identify predictive factors within the structured data set that are correlated to an objective corresponding to the set of response data; 
 generate a correlated structured data set based at least in part on the structured data set and the identified predictive factors; 
 generate a standardized lemma data set by converting terms within the free text data set into a plurality of lemmas; 
 generate a filtered lemma data set by filtering the standardized lemma data set to remove terms that are not associated with nouns or adjectives; 
 generate a scored lemma data set based at least in part on determining scores for the filtered lemma data set in view of the objective; 
 analyze the plurality of lemmas in the scored lemma data set to determine:
 occurrences of lemmas associated with a positive objective value; and 
 occurrences of lemmas associated with a negative objective value; 
 
 generate a distributed lemma data set by balancing distribution of the scored lemma data set against an aggregate frequency of terms in the scored lemma data set; and 
 assign polarity values to terms in the distributed lemma data set, wherein the polarity values indicate whether each of the terms is a positive term or a negative term based at least in part on a determination of each of the terms being associated with a degree of impact on one or more outcomes; 
 generate a recommendation action based at least in part on the polarity values. 
 
     
     
       18. The non-transitory computer storage of  claim 17 , wherein the set of response data is based at least in part on an aggregated customer feedback dataset. 
     
     
       19. The non-transitory computer storage of  claim 17 , wherein the identified predictive factors also indicate one or more behavior patterns associated with the first objective. 
     
     
       20. The non-transitory computer storage of  claim 17 , the executable instructions further configured to instruct the computer system to at least:
 aggregate the filtered lemma data set with the correlated structured data set.